Subjective Response Comparison
Linear Mixed Model Regression
Hypothesis 1A: Predicted Values of Subjective Response by Yoni Task
df <- data %>%
mutate(participant = as.factor(participant),
ConditionSP = as.factor(ConditionSP)) %>%
filter(truthfulness == "LIE") %>%
filter(Inclusion2 != "0") %>%
filter(participant != "3",
participant != "15",
participant != "19",
participant != "23") %>%
filter(Condition == "Q") %>%
mutate(yoni_new = yoni_affective + yoni_cognitive)
df$ConditionSP <- relevel(df$ConditionSP, ref = "social")
# TOM Hypothesis 1A: SUBRESP -> YONI
model_sub1 <- lme4::lmer(subresp ~ ConditionSP * yoni_new + (1|participant), data = df) #explorative
parameters::parameters(model_sub1)
| (Intercept) |
6.8528204 |
9.8681562 |
0.95 |
-12.4884103 |
26.1940510 |
0.6944378 |
948 |
0.4874077 |
fixed |
|
| ConditionSPpolygraph |
-8.3898130 |
3.9530534 |
0.95 |
-16.1376553 |
-0.6419706 |
-2.1223627 |
948 |
0.0338073 |
fixed |
|
| yoni_new |
-0.0751755 |
0.1347756 |
0.95 |
-0.3393307 |
0.1889797 |
-0.5577828 |
948 |
0.5769927 |
fixed |
|
| ConditionSPpolygraph:yoni_new |
0.1193514 |
0.0540438 |
0.95 |
0.0134274 |
0.2252753 |
2.2084178 |
948 |
0.0272152 |
fixed |
|
| SD (Intercept) |
4.0785473 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2695234 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_sub1, type = "pred", terms = c("yoni_new", "ConditionSP"), axis.title = c("Yoni Task", "Subjective Response"), title = "Predicted Values of Subjective Response by Yoni Task", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and yoni_new (formula: subresp ~ ConditionSP * yoni_new). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 3.92e-03. The model's intercept, corresponding to ConditionSP = social and yoni_new = 0, is at 6.85 (95% CI [-12.49, 26.19], t(948) = 0.69, p = 0.487). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -8.39, 95% CI [-16.14, -0.64], t(948) = -2.12, p < .05; Std. beta = 0.05, 95% CI [-0.05, 0.15])
## - The effect of yoni_new is statistically non-significant and negative (beta = -0.08, 95% CI [-0.34, 0.19], t(948) = -0.56, p = 0.577; Std. beta = -0.07, 95% CI [-0.32, 0.18])
## - The interaction effect of yoni_new on ConditionSP [polygraph] is statistically significant and positive (beta = 0.12, 95% CI [0.01, 0.23], t(948) = 2.21, p < .05; Std. beta = 0.11, 95% CI [0.01, 0.22])
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -8.39, 95% CI [-16.14, -0.64], t(948) = -2.12, p < .05; Std. beta = 0.89, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 1A: Predicted Values of Subjective Response by BES Questionnaire
# TOM Hypothesis 1A: SUBRESP -> YONI/BES
model_sub2 <- lme4::lmer(subresp ~ ConditionSP * BES_Total + (1|participant), data = df)
parameters::parameters(model_sub2)
| (Intercept) |
3.3794418 |
6.9891128 |
0.95 |
-10.3189676 |
17.0778513 |
0.4835294 |
948 |
0.6287199 |
fixed |
|
| ConditionSPpolygraph |
-7.4937624 |
2.8359518 |
0.95 |
-13.0521257 |
-1.9353991 |
-2.6424153 |
948 |
0.0082317 |
fixed |
|
| BES_Total |
-0.0275047 |
0.0948164 |
0.95 |
-0.2133415 |
0.1583321 |
-0.2900839 |
948 |
0.7717521 |
fixed |
|
| ConditionSPpolygraph:BES_Total |
0.1068273 |
0.0385568 |
0.95 |
0.0312572 |
0.1823973 |
2.7706434 |
948 |
0.0055946 |
fixed |
|
| SD (Intercept) |
4.0810687 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2677728 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_sub2, type = "pred", terms = c("BES_Total", "ConditionSP"), axis.title = c("BES Questionnaire", "Subjective Response"), title = "Predicted Values of Subjective Response by BES Questionnaire", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and BES_Total (formula: subresp ~ ConditionSP * BES_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 6.50e-03. The model's intercept, corresponding to ConditionSP = social and BES_Total = 0, is at 3.38 (95% CI [-10.32, 17.08], t(948) = 0.48, p = 0.629). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -7.49, 95% CI [-13.05, -1.94], t(948) = -2.64, p < .01; Std. beta = 0.05, 95% CI [-0.05, 0.15])
## - The effect of BES_Total is statistically non-significant and negative (beta = -0.03, 95% CI [-0.21, 0.16], t(948) = -0.29, p = 0.772; Std. beta = -0.04, 95% CI [-0.29, 0.21])
## - The interaction effect of BES_Total on ConditionSP [polygraph] is statistically significant and positive (beta = 0.11, 95% CI [0.03, 0.18], t(948) = 2.77, p < .01; Std. beta = 0.14, 95% CI [0.04, 0.24])
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -7.49, 95% CI [-13.05, -1.94], t(948) = -2.64, p < .01; Std. beta = 0.89, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 1C: Predicted Values of Subjective Response by SRP3
#TOM Hypothesis 1C: SUBRESP -> SRP3
model_sub3 <- lme4::lmer(subresp ~ ConditionSP * SRP3_Total + (1|participant), data = df)
parameters::parameters(model_sub3)
| (Intercept) |
-4.0315924 |
4.5094050 |
0.95 |
-12.8698637 |
4.8066789 |
-0.8940409 |
948 |
0.3713000 |
fixed |
|
| ConditionSPpolygraph |
0.9748765 |
1.8454061 |
0.95 |
-2.6420530 |
4.5918059 |
0.5282721 |
948 |
0.5973105 |
fixed |
|
| SRP3_Total |
0.0383807 |
0.0315086 |
0.95 |
-0.0233749 |
0.1001364 |
1.2181040 |
948 |
0.2231844 |
fixed |
|
| ConditionSPpolygraph:SRP3_Total |
-0.0047447 |
0.0129171 |
0.95 |
-0.0300617 |
0.0205724 |
-0.3673178 |
948 |
0.7133820 |
fixed |
|
| SD (Intercept) |
3.9616800 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2724345 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_sub3, type = "pred", terms = c("SRP3_Total", "ConditionSP"), axis.title = c("SRP3 Inventory", "Subjective Response"), title = "Predicted Values of Subjective Response by SRP3 Inventory", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and SRP3_Total (formula: subresp ~ ConditionSP * SRP3_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model's intercept, corresponding to ConditionSP = social and SRP3_Total = 0, is at -4.03 (95% CI [-12.87, 4.81], t(948) = -0.89, p = 0.371). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.97, 95% CI [-2.64, 4.59], t(948) = 0.53, p = 0.597; Std. beta = 0.05, 95% CI [-0.05, 0.15])
## - The effect of SRP3_Total is statistically non-significant and positive (beta = 0.04, 95% CI [-0.02, 0.10], t(948) = 1.22, p = 0.223; Std. beta = 0.15, 95% CI [-0.09, 0.40])
## - The interaction effect of SRP3_Total on ConditionSP [polygraph] is statistically non-significant and negative (beta = -4.74e-03, 95% CI [-0.03, 0.02], t(948) = -0.37, p = 0.713; Std. beta = -0.02, 95% CI [-0.12, 0.08])
## - The effect of ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.97, 95% CI [-2.64, 4.59], t(948) = 0.53, p = 0.597; Std. beta = 0.89, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 2A: Predicted Values of Subjective Response by HCT Accuracy
df$ConditionSP <- relevel(df$ConditionSP, ref = "polygraph")
#Interoception Hypothesis 2A: SUBRESP -> HCT
model_sub4 <- lme4::lmer(subresp ~ ConditionSP * HCT_Accuracy + (1|participant), data = df)
parameters::parameters(model_sub4)
| (Intercept) |
18.488796 |
11.851499 |
0.95 |
-4.739715 |
41.717308 |
1.560039 |
948 |
0.1187508 |
fixed |
|
| ConditionSPsocial |
-12.414708 |
4.823371 |
0.95 |
-21.868340 |
-2.961075 |
-2.573866 |
948 |
0.0100569 |
fixed |
|
| HCT_Accuracy |
-18.223889 |
12.817837 |
0.95 |
-43.346387 |
6.898610 |
-1.421760 |
948 |
0.1550959 |
fixed |
|
| ConditionSPsocial:HCT_Accuracy |
13.128547 |
5.218345 |
0.95 |
2.900778 |
23.356316 |
2.515845 |
948 |
0.0118747 |
fixed |
|
| SD (Intercept) |
4.010104 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.268615 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_sub4, type = "pred", terms = c("HCT_Accuracy", "ConditionSP"), axis.title = c("HCT Accuracy", "Subjective Response"), title = "Predicted Values of Subjective Response by HCT Accuracy", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and HCT_Accuracy (formula: subresp ~ ConditionSP * HCT_Accuracy). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model's intercept, corresponding to ConditionSP = polygraph and HCT_Accuracy = 0, is at 18.49 (95% CI [-4.74, 41.72], t(948) = 1.56, p = 0.119). Within this model:
##
## - The effect of ConditionSP [social] is statistically significant and negative (beta = -12.41, 95% CI [-21.87, -2.96], t(948) = -2.57, p < .05; Std. beta = -0.05, 95% CI [-0.15, 0.05])
## - The effect of HCT_Accuracy is statistically non-significant and negative (beta = -18.22, 95% CI [-43.35, 6.90], t(948) = -1.42, p = 0.155; Std. beta = -0.18, 95% CI [-0.43, 0.07])
## - The interaction effect of HCT_Accuracy on ConditionSP [social] is statistically significant and positive (beta = 13.13, 95% CI [2.90, 23.36], t(948) = 2.52, p < .05; Std. beta = 0.13, 95% CI [0.03, 0.23])
## - The effect of ConditionSP [social] is statistically significant and negative (beta = -12.41, 95% CI [-21.87, -2.96], t(948) = -2.57, p < .05; Std. beta = 0.89, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 2A: Predicted Values of Subjective Response by MAIA2
#Interoception Hypothesis 2A: SUBRESP -> MAIA2
model_sub5 <- lme4::lmer(subresp ~ ConditionSP * MAIA_Total + (1|participant), data = df)
parameters::parameters(model_sub5)
| (Intercept) |
8.582359 |
4.0502482 |
0.95 |
0.6440189 |
16.5206999 |
2.118971 |
948 |
0.0340929 |
fixed |
|
| ConditionSPsocial |
-3.249959 |
1.6862691 |
0.95 |
-6.5549858 |
0.0550675 |
-1.927308 |
948 |
0.0539413 |
fixed |
|
| MAIA_Total |
-2.580371 |
1.4838168 |
0.95 |
-5.4885989 |
0.3278560 |
-1.739010 |
948 |
0.0820331 |
fixed |
|
| ConditionSPsocial:MAIA_Total |
1.101971 |
0.6193228 |
0.95 |
-0.1118797 |
2.3158209 |
1.779316 |
948 |
0.0751880 |
fixed |
|
| SD (Intercept) |
3.908004 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.270618 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_sub5, type = "pred", terms = c("MAIA_Total", "ConditionSP"), axis.title = c("MAIA2", "Subjective Response"), title = "Predicted Values of Subjective Response by MAIA2", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and MAIA_Total (formula: subresp ~ ConditionSP * MAIA_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model's intercept, corresponding to ConditionSP = polygraph and MAIA_Total = 0, is at 8.58 (95% CI [0.64, 16.52], t(948) = 2.12, p < .05). Within this model:
##
## - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -3.25, 95% CI [-6.55, 0.06], t(948) = -1.93, p = 0.054; Std. beta = -0.05, 95% CI [-0.15, 0.05])
## - The effect of MAIA_Total is statistically non-significant and negative (beta = -2.58, 95% CI [-5.49, 0.33], t(948) = -1.74, p = 0.082; Std. beta = -0.22, 95% CI [-0.46, 0.03])
## - The interaction effect of MAIA_Total on ConditionSP [social] is statistically non-significant and positive (beta = 1.10, 95% CI [-0.11, 2.32], t(948) = 1.78, p = 0.075; Std. beta = 0.09, 95% CI [-9.34e-03, 0.19])
## - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -3.25, 95% CI [-6.55, 0.06], t(948) = -1.93, p = 0.054; Std. beta = 0.89, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 2C: Predicted Values of Subjective Response by Primary SRP3 Subfactor
#Interoception Hypothesis 2C: SUBRESP -> SRP3_PRI
model_sub6 <- lme4::lmer(subresp ~ ConditionSP * SRP3_PRI + (1|participant), data = df)
parameters::parameters(model_sub6)
| (Intercept) |
-3.2763117 |
3.9672390 |
0.95 |
-11.0519573 |
4.4993339 |
-0.8258418 |
948 |
0.4088939 |
fixed |
|
| ConditionSPsocial |
-0.5546161 |
1.6372337 |
0.95 |
-3.7635352 |
2.6543029 |
-0.3387520 |
948 |
0.7347966 |
fixed |
|
| SRP3_PRI |
0.0631348 |
0.0494939 |
0.95 |
-0.0338714 |
0.1601410 |
1.2756093 |
948 |
0.2020937 |
fixed |
|
| ConditionSPsocial:SRP3_PRI |
0.0031487 |
0.0204673 |
0.95 |
-0.0369665 |
0.0432639 |
0.1538397 |
948 |
0.8777362 |
fixed |
|
| SD (Intercept) |
3.9267513 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2725081 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_sub6, type = "pred", terms = c("SRP3_PRI", "ConditionSP"), axis.title = c("SRP3 (PRI) Inventory", "Subjective Response"), title = "Predicted Values of Subjective Response by SRP3 (PRI)", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict subresp with ConditionSP and SRP3_PRI (formula: subresp ~ ConditionSP * SRP3_PRI). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model's intercept, corresponding to ConditionSP = polygraph and SRP3_PRI = 0, is at -3.28 (95% CI [-11.05, 4.50], t(948) = -0.83, p = 0.409). Within this model:
##
## - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -0.55, 95% CI [-3.76, 2.65], t(948) = -0.34, p = 0.735; Std. beta = -0.05, 95% CI [-0.15, 0.05])
## - The effect of SRP3_PRI is statistically non-significant and positive (beta = 0.06, 95% CI [-0.03, 0.16], t(948) = 1.28, p = 0.202; Std. beta = 0.16, 95% CI [-0.09, 0.41])
## - The interaction effect of SRP3_PRI on ConditionSP [social] is statistically non-significant and positive (beta = 3.15e-03, 95% CI [-0.04, 0.04], t(948) = 0.15, p = 0.878; Std. beta = 7.97e-03, 95% CI [-0.09, 0.11])
## - The effect of ConditionSP [social] is statistically non-significant and negative (beta = -0.55, 95% CI [-3.76, 2.65], t(948) = -0.34, p = 0.735; Std. beta = 0.89, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
ECG Physio scores comparison
Linear Mixed Model Regression
Hypothesis 1A: Predicted Values of Heartrate Acceleration by Yoni Task
df2 <- data %>%
mutate(participant = as.factor(participant),
ConditionSP = as.factor(ConditionSP)) %>%
filter(truthfulness == "LIE") %>%
filter(Inclusion2 != "0") %>%
# filter(participant != "22") %>%
# filter(participant != "23") %>%
filter(participant != "30") %>%
filter(participant != "15") %>%
filter(Condition == "Q") %>%
mutate(yoni_new = yoni_affective + yoni_cognitive)
df2$ConditionSP <- relevel(df2$ConditionSP, ref = "social")
#ECG_RATE_MAX
#TOM Hypothesis 1A: ECG_Rate_Max -> YONI
model_ecg1 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * yoni_new + (1|participant), data = df2) #explorative
parameters::parameters(model_ecg1)
| (Intercept) |
27.2578083 |
7.1631515 |
0.95 |
13.2182894 |
41.2973273 |
3.8052816 |
1018 |
0.0001416 |
fixed |
|
| ConditionSPpolygraph |
-2.1507216 |
3.6532777 |
0.95 |
-9.3110143 |
5.0095712 |
-0.5887101 |
1018 |
0.5560558 |
fixed |
|
| yoni_new |
-0.2572554 |
0.0982664 |
0.95 |
-0.4498541 |
-0.0646567 |
-2.6179373 |
1018 |
0.0088463 |
fixed |
|
| ConditionSPpolygraph:yoni_new |
-0.0015613 |
0.0501970 |
0.95 |
-0.0999455 |
0.0968230 |
-0.0311027 |
1018 |
0.9751876 |
fixed |
|
| SD (Intercept) |
3.2041857 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2969355 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_ecg1, type = "pred", terms = c("yoni_new", "ConditionSP"), axis.title = c("Yoni Task", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by Yoni Task ", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and yoni_new (formula: ECG_Rate_Max ~ ConditionSP * yoni_new). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.10. The model's intercept, corresponding to ConditionSP = social and yoni_new = 0, is at 27.26 (95% CI [13.22, 41.30], t(1018) = 3.81, p < .001). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -2.15, 95% CI [-9.31, 5.01], t(1018) = -0.59, p = 0.556; Std. beta = -0.35, 95% CI [-0.45, -0.25])
## - The effect of yoni_new is statistically significant and negative (beta = -0.26, 95% CI [-0.45, -0.06], t(1018) = -2.62, p < .01; Std. beta = -0.26, 95% CI [-0.46, -0.07])
## - The interaction effect of yoni_new on ConditionSP [polygraph] is statistically non-significant and negative (beta = -1.56e-03, 95% CI [-0.10, 0.10], t(1018) = -0.03, p = 0.975; Std. beta = -1.60e-03, 95% CI [-0.10, 0.10])
## - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -2.15, 95% CI [-9.31, 5.01], t(1018) = -0.59, p = 0.556; Std. beta = 0.91, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 1A: Predicted Values of Heartrate Acceleration by BES Questionniare
#TOM Hypothesis 1A: ECG_Rate_Max -> BES
model_ecg2 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * BES_Total + (1|participant), data = df2)
parameters::parameters(model_ecg2)
| (Intercept) |
0.2052575 |
5.5806691 |
0.95 |
-10.7326530 |
11.1431681 |
0.0367801 |
1018 |
0.9706603 |
fixed |
|
| ConditionSPpolygraph |
-3.7951969 |
2.6802022 |
0.95 |
-9.0482967 |
1.4579029 |
-1.4160114 |
1018 |
0.1567722 |
fixed |
|
| BES_Total |
0.1138884 |
0.0752970 |
0.95 |
-0.0336909 |
0.2614677 |
1.5125233 |
1018 |
0.1304008 |
fixed |
|
| ConditionSPpolygraph:BES_Total |
0.0208820 |
0.0361894 |
0.95 |
-0.0500480 |
0.0918119 |
0.5770187 |
1018 |
0.5639268 |
fixed |
|
| SD (Intercept) |
3.4646819 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2967229 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_ecg2, type = "pred", terms = c("BES_Total", "ConditionSP"), axis.title = c("BES Questionnaire", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by BES Questionnaire", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and BES_Total (formula: ECG_Rate_Max ~ ConditionSP * BES_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model's intercept, corresponding to ConditionSP = social and BES_Total = 0, is at 0.21 (95% CI [-10.73, 11.14], t(1018) = 0.04, p = 0.971). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -3.80, 95% CI [-9.05, 1.46], t(1018) = -1.42, p = 0.157; Std. beta = -0.35, 95% CI [-0.45, -0.25])
## - The effect of BES_Total is statistically non-significant and positive (beta = 0.11, 95% CI [-0.03, 0.26], t(1018) = 1.51, p = 0.130; Std. beta = 0.16, 95% CI [-0.05, 0.37])
## - The interaction effect of BES_Total on ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.05, 0.09], t(1018) = 0.58, p = 0.564; Std. beta = 0.03, 95% CI [-0.07, 0.13])
## - The effect of ConditionSP [polygraph] is statistically non-significant and negative (beta = -3.80, 95% CI [-9.05, 1.46], t(1018) = -1.42, p = 0.157; Std. beta = 0.91, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 1C: Predicted Values of Heartrate Acceleration by SRP3
#TOM Hypothesis 1C: ECG_Rate_Max -> SRP3
model_ecg3 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * SRP3_Total + (1|participant), data = df2)
parameters::parameters(model_ecg3)
| (Intercept) |
16.2949842 |
3.8878843 |
0.95 |
8.6748710 |
23.9150974 |
4.191222 |
1018 |
0.0000277 |
fixed |
|
| ConditionSPpolygraph |
-6.0812362 |
1.8276611 |
0.95 |
-9.6633862 |
-2.4990862 |
-3.327332 |
1018 |
0.0008768 |
fixed |
|
| SRP3_Total |
-0.0537453 |
0.0266153 |
0.95 |
-0.1059103 |
-0.0015802 |
-2.019337 |
1018 |
0.0434522 |
fixed |
|
| ConditionSPpolygraph:SRP3_Total |
0.0266865 |
0.0125555 |
0.95 |
0.0020781 |
0.0512948 |
2.125479 |
1018 |
0.0335467 |
fixed |
|
| SD (Intercept) |
3.4893102 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2943489 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_ecg3, type = "pred", terms = c("SRP3_Total", "ConditionSP"), axis.title = c("SRP3 Inventory", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by SRP3 Inventory", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and SRP3_Total (formula: ECG_Rate_Max ~ ConditionSP * SRP3_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.35) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model's intercept, corresponding to ConditionSP = social and SRP3_Total = 0, is at 16.29 (95% CI [8.67, 23.92], t(1018) = 4.19, p < .001). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.08, 95% CI [-9.66, -2.50], t(1018) = -3.33, p < .001; Std. beta = -0.35, 95% CI [-0.45, -0.25])
## - The effect of SRP3_Total is statistically significant and negative (beta = -0.05, 95% CI [-0.11, -1.58e-03], t(1018) = -2.02, p < .05; Std. beta = -0.22, 95% CI [-0.43, -6.47e-03])
## - The interaction effect of SRP3_Total on ConditionSP [polygraph] is statistically significant and positive (beta = 0.03, 95% CI [2.08e-03, 0.05], t(1018) = 2.13, p < .05; Std. beta = 0.11, 95% CI [8.50e-03, 0.21])
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.08, 95% CI [-9.66, -2.50], t(1018) = -3.33, p < .001; Std. beta = 0.91, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 2A: Predicted Values of Heartrate Acceleration by HCT Accuracy
df2$ConditionSP <- relevel(df2$ConditionSP, ref = "social")
#Interoception Hypothesis 2A: ECG_Rate_Max -> HCT
model_ecg4 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * HCT_Accuracy + (1|participant), data = df2)
parameters::parameters(model_ecg4)
| (Intercept) |
35.956078 |
9.809649 |
0.95 |
16.729519 |
55.182637 |
3.665379 |
1018 |
0.0002470 |
fixed |
|
| ConditionSPpolygraph |
-10.158290 |
4.912066 |
0.95 |
-19.785763 |
-0.530817 |
-2.068028 |
1018 |
0.0386374 |
fixed |
|
| HCT_Accuracy |
-29.602954 |
10.583262 |
0.95 |
-50.345766 |
-8.860142 |
-2.797149 |
1018 |
0.0051556 |
fixed |
|
| ConditionSPpolygraph:HCT_Accuracy |
8.541931 |
5.301230 |
0.95 |
-1.848289 |
18.932150 |
1.611311 |
1018 |
0.1071119 |
fixed |
|
| SD (Intercept) |
3.265593 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.295488 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_ecg4, type = "pred", terms = c("HCT_Accuracy", "ConditionSP"), axis.title = c("HCT Accuracy", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by HCT Accuracy", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and HCT_Accuracy (formula: ECG_Rate_Max ~ ConditionSP * HCT_Accuracy). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model's intercept, corresponding to ConditionSP = social and HCT_Accuracy = 0, is at 35.96 (95% CI [16.73, 55.18], t(1018) = 3.67, p < .001). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -10.16, 95% CI [-19.79, -0.53], t(1018) = -2.07, p < .05; Std. beta = -0.35, 95% CI [-0.45, -0.25])
## - The effect of HCT_Accuracy is statistically significant and negative (beta = -29.60, 95% CI [-50.35, -8.86], t(1018) = -2.80, p < .01; Std. beta = -0.29, 95% CI [-0.49, -0.09])
## - The interaction effect of HCT_Accuracy on ConditionSP [polygraph] is statistically non-significant and positive (beta = 8.54, 95% CI [-1.85, 18.93], t(1018) = 1.61, p = 0.107; Std. beta = 0.08, 95% CI [-0.02, 0.18])
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -10.16, 95% CI [-19.79, -0.53], t(1018) = -2.07, p < .05; Std. beta = 0.91, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 2A: Predicted Values of Heartrate Acceleration by MAIA2
#Interoception Hypothesis 2A: ECG_Rate_Max -> MAIA2
model_ecg5 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * MAIA_Total + (1|participant), data = df2)
parameters::parameters(model_ecg5)
| (Intercept) |
7.6680831 |
4.0216640 |
0.95 |
-0.2142336 |
15.5503998 |
1.9066941 |
1018 |
0.0565602 |
fixed |
|
| ConditionSPpolygraph |
-3.6884331 |
1.8236835 |
0.95 |
-7.2627871 |
-0.1140792 |
-2.0225182 |
1018 |
0.0431228 |
fixed |
|
| MAIA_Total |
0.3446661 |
1.4974227 |
0.95 |
-2.5902284 |
3.2795605 |
0.2301729 |
1018 |
0.8179574 |
fixed |
|
| ConditionSPpolygraph:MAIA_Total |
0.5411528 |
0.6797932 |
0.95 |
-0.7912174 |
1.8735231 |
0.7960551 |
1018 |
0.4260000 |
fixed |
|
| SD (Intercept) |
3.6504185 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2965449 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_ecg5, type = "pred", terms = c("MAIA_Total", "ConditionSP"), axis.title = c("MAIA2", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by MAIA2", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and MAIA_Total (formula: ECG_Rate_Max ~ ConditionSP * MAIA_Total). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.35) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model's intercept, corresponding to ConditionSP = social and MAIA_Total = 0, is at 7.67 (95% CI [-0.21, 15.55], t(1018) = 1.91, p = 0.057). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -3.69, 95% CI [-7.26, -0.11], t(1018) = -2.02, p < .05; Std. beta = -0.35, 95% CI [-0.45, -0.25])
## - The effect of MAIA_Total is statistically non-significant and positive (beta = 0.34, 95% CI [-2.59, 3.28], t(1018) = 0.23, p = 0.818; Std. beta = 0.03, 95% CI [-0.20, 0.25])
## - The interaction effect of MAIA_Total on ConditionSP [polygraph] is statistically non-significant and positive (beta = 0.54, 95% CI [-0.79, 1.87], t(1018) = 0.80, p = 0.426; Std. beta = 0.04, 95% CI [-0.06, 0.14])
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -3.69, 95% CI [-7.26, -0.11], t(1018) = -2.02, p < .05; Std. beta = 0.91, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Hypothesis 2C: Predicted Values of Heartrate Acceleration by Primary SRP3 Subfactor
#Interoception Hypothesis 2C: ECG_Rate_Max -> SRP3_PRI
model_ecg6 <- lme4::lmer(ECG_Rate_Max ~ ConditionSP * SRP3_PRI + (1|participant), data = df2)
parameters::parameters(model_ecg6)
| (Intercept) |
15.0477509 |
3.4131857 |
0.95 |
8.3580299 |
21.7374720 |
4.408711 |
1018 |
0.0000104 |
fixed |
|
| ConditionSPpolygraph |
-6.1764211 |
1.5915639 |
0.95 |
-9.2958289 |
-3.0570132 |
-3.880725 |
1018 |
0.0001041 |
fixed |
|
| SRP3_PRI |
-0.0805672 |
0.0415477 |
0.95 |
-0.1619991 |
0.0008647 |
-1.939152 |
1018 |
0.0524828 |
fixed |
|
| ConditionSPpolygraph:SRP3_PRI |
0.0488934 |
0.0194421 |
0.95 |
0.0107876 |
0.0869992 |
2.514821 |
1018 |
0.0119093 |
fixed |
|
| SD (Intercept) |
3.5208834 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
participant |
| SD (Observations) |
2.2933294 |
NA |
0.95 |
NA |
NA |
NA |
NA |
NA |
random |
Residual |
plot_model(model_ecg6, type = "pred", terms = c("SRP3_PRI", "ConditionSP"), axis.title = c("SRP3 (PRI) Inventory", "Heartrate Acceleration"), title = "Predicted Values of Heartrate Acceleration by SRP3 (PRI) ", legend.title = "Condition") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ECG_Rate_Max with ConditionSP and SRP3_PRI (formula: ECG_Rate_Max ~ ConditionSP * SRP3_PRI). The model included participant as random effect (formula: ~1 | participant). The model's total explanatory power is substantial (conditional R2 = 0.35) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model's intercept, corresponding to ConditionSP = social and SRP3_PRI = 0, is at 15.05 (95% CI [8.36, 21.74], t(1018) = 4.41, p < .001). Within this model:
##
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.18, 95% CI [-9.30, -3.06], t(1018) = -3.88, p < .001; Std. beta = -0.35, 95% CI [-0.45, -0.25])
## - The effect of SRP3_PRI is statistically non-significant and negative (beta = -0.08, 95% CI [-0.16, 8.65e-04], t(1018) = -1.94, p = 0.052; Std. beta = -0.21, 95% CI [-0.43, 2.28e-03])
## - The interaction effect of SRP3_PRI on ConditionSP [polygraph] is statistically significant and positive (beta = 0.05, 95% CI [0.01, 0.09], t(1018) = 2.51, p < .05; Std. beta = 0.13, 95% CI [0.03, 0.23])
## - The effect of ConditionSP [polygraph] is statistically significant and negative (beta = -6.18, 95% CI [-9.30, -3.06], t(1018) = -3.88, p < .001; Std. beta = 0.91, )
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.
Correalation Analysis
Hypothesis 1B: Correlation Between ToM and Psychopathy
df5 <- data %>%
mutate(yoni_new = yoni_affective + yoni_cognitive) %>%
group_by(participant) %>%
summarise_all(mean)
#Hypothesis 1b
##Yoni Task (1b)
model_H1 <- lm(yoni_affective ~ SRP3_Total, data = df5)
parameters::parameters(model_H1)
| (Intercept) |
42.2131672 |
4.2344744 |
0.95 |
33.5392396 |
50.8870948 |
9.9689273 |
28 |
0.0000000 |
| SRP3_Total |
-0.0026549 |
0.0291224 |
0.95 |
-0.0623096 |
0.0569997 |
-0.0911648 |
28 |
0.9280107 |
plot_model(model_H1, type = "pred", terms = c("SRP3_Total"), axis.title = c("SRP3 Inventory", "Yoni Task (Affective)"), title = "Relationship Between Yoni Task (Affective) and SRP3") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear model (estimated using OLS) to predict yoni_affective with SRP3_Total (formula: yoni_affective ~ SRP3_Total). The model explains a statistically not significant and very weak proportion of variance (R2 = 2.97e-04, F(1, 28) = 8.31e-03, p = 0.928, adj. R2 = -0.04). The model's intercept, corresponding to SRP3_Total = 0, is at 42.21 (95% CI [33.54, 50.89], t(28) = 9.97, p < .001). Within this model:
##
## - The effect of SRP3_Total is statistically non-significant and negative (beta = -2.65e-03, 95% CI [-0.06, 0.06], t(28) = -0.09, p = 0.928; Std. beta = -0.02, 95% CI [-0.40, 0.37])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
yoni_affective,
SRP3_Total), method = "pearson")
| yoni_affective |
SRP3_Total |
-0.017226 |
0.95 |
-0.3751669 |
0.3451855 |
-0.0911648 |
28 |
0.9280107 |
Pearson correlation |
30 |
##Basic Empathy Scale (1b)
model_H1a <- lm(BES_Affective ~ SRP3_Total, data = df5)
parameters::parameters(model_H1a)
| (Intercept) |
59.0685800 |
6.2207642 |
0.95 |
46.3259222 |
71.8112378 |
9.495390 |
28 |
0.000000 |
| SRP3_Total |
-0.1470311 |
0.0427831 |
0.95 |
-0.2346683 |
-0.0593939 |
-3.436664 |
28 |
0.001858 |
plot_model(model_H1a, type = "pred", terms = ("SRP3_Total"), axis.title = c("SRP3 Inventory", "BES (Affective)"), title = "Relationship Between BES (Affective) Score and SRP3") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear model (estimated using OLS) to predict BES_Affective with SRP3_Total (formula: BES_Affective ~ SRP3_Total). The model explains a statistically significant and substantial proportion of variance (R2 = 0.30, F(1, 28) = 11.81, p = 0.002, adj. R2 = 0.27). The model's intercept, corresponding to SRP3_Total = 0, is at 59.07 (95% CI [46.33, 71.81], t(28) = 9.50, p < .001). Within this model:
##
## - The effect of SRP3_Total is statistically significant and negative (beta = -0.15, 95% CI [-0.23, -0.06], t(28) = -3.44, p < .01; Std. beta = -0.54, 95% CI [-0.87, -0.22])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
BES_Affective,
SRP3_Total), method = "pearson")
| BES_Affective |
SRP3_Total |
-0.5446749 |
0.95 |
-0.756497 |
-0.2294259 |
-3.436664 |
28 |
0.001858 |
Pearson correlation |
30 |
Hypothesis 2B: Correlation Between Interoception and Primary Psychopathy
#Hypothesis 2b
##Heartbeat Counting Task(2b)
model_H2 <- lm(HCT_Accuracy ~ SRP3_PRI, data = df5)
parameters::parameters(model_H2)
| (Intercept) |
0.8561677 |
0.0550071 |
0.95 |
0.7434907 |
0.9688446 |
15.564671 |
28 |
0.0000000 |
| SRP3_PRI |
0.0008599 |
0.0006722 |
0.95 |
-0.0005169 |
0.0022368 |
1.279378 |
28 |
0.2112645 |
plot_model(model_H2, type = "pred", terms = ("SRP3_PRI"), axis.title = c("SRP3 (PRI) Inventory", "HCT Accuracy"), title = "Relationship Between HCT Accuracy and SRP3 (PRI)") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear model (estimated using OLS) to predict HCT_Accuracy with SRP3_PRI (formula: HCT_Accuracy ~ SRP3_PRI). The model explains a statistically not significant and weak proportion of variance (R2 = 0.06, F(1, 28) = 1.64, p = 0.211, adj. R2 = 0.02). The model's intercept, corresponding to SRP3_PRI = 0, is at 0.86 (95% CI [0.74, 0.97], t(28) = 15.56, p < .001). Within this model:
##
## - The effect of SRP3_PRI is statistically non-significant and positive (beta = 8.60e-04, 95% CI [-5.17e-04, 2.24e-03], t(28) = 1.28, p = 0.211; Std. beta = 0.24, 95% CI [-0.14, 0.61])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
HCT_Accuracy,
SRP3_PRI), method = "pearson")
| HCT_Accuracy |
SRP3_PRI |
0.2350084 |
0.95 |
-0.1368472 |
0.5488117 |
1.279378 |
28 |
0.2112645 |
Pearson correlation |
30 |
##MAIA2 (2b)
model_H2a <- lm(MAIA_Total ~ SRP3_PRI, data = df5)
parameters::parameters(model_H2)
| (Intercept) |
0.8561677 |
0.0550071 |
0.95 |
0.7434907 |
0.9688446 |
15.564671 |
28 |
0.0000000 |
| SRP3_PRI |
0.0008599 |
0.0006722 |
0.95 |
-0.0005169 |
0.0022368 |
1.279378 |
28 |
0.2112645 |
plot_model(model_H2, type = "pred", terms = ("SRP3_PRI"), axis.title = c("SRP3 (PRI) Inventory", "MAIA2"), title = "Relationship Between MAIA2 and SRP3 (PRI)") + theme_sjplot(base_size = 10, base_family = "Times New Roman")

## We fitted a linear model (estimated using OLS) to predict MAIA_Total with SRP3_PRI (formula: MAIA_Total ~ SRP3_PRI). The model explains a statistically not significant and weak proportion of variance (R2 = 0.03, F(1, 28) = 0.93, p = 0.343, adj. R2 = -2.39e-03). The model's intercept, corresponding to SRP3_PRI = 0, is at 3.18 (95% CI [2.16, 4.19], t(28) = 6.42, p < .001). Within this model:
##
## - The effect of SRP3_PRI is statistically non-significant and negative (beta = -5.83e-03, 95% CI [-0.02, 6.55e-03], t(28) = -0.96, p = 0.343; Std. beta = -0.18, 95% CI [-0.56, 0.20])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
correlation::correlation(select(df5,
MAIA_Total,
SRP3_PRI), method = "pearson")
| MAIA_Total |
SRP3_PRI |
-0.1793627 |
0.95 |
-0.5068781 |
0.193404 |
-0.9647436 |
28 |
0.3429322 |
Pearson correlation |
30 |
Correlation Between Yoni Task and Basic Empathy Scale
#Correlation between Yoni Task and BES Scale
correlation::correlation(select(df5,
yoni_new,
BES_Total), method = "pearson")
| yoni_new |
BES_Total |
0.0678549 |
0.95 |
-0.2997418 |
0.4179079 |
0.3598838 |
28 |
0.7216344 |
Pearson correlation |
30 |
correlation::correlation(select(df5,
yoni_affective,
BES_Affective), method = "pearson")
| yoni_affective |
BES_Affective |
-0.0164488 |
0.95 |
-0.3744987 |
0.34587 |
-0.0870507 |
28 |
0.9312508 |
Pearson correlation |
30 |